Shijie Wu

Artificial Intelligence · with Dredze · 2019–2023

A researcher in the Science corpus: 6 papers; trusts 167 researchers and is trusted by 70 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 189 citations into it; #3,583 of 35,345 by trust here, #5,187 by citations
• Natural Language Processing — 155 citations into it
• Computer Vision and Pattern Recognition — 55 citations into it
• Large Language Models — 52 citations into it; #112 of 799 by trust here, #443 by citations
• Evaluation & Benchmarks — 40 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Model Architectures — 26 citations into it
• Management Science and Operations Research — 11 citations into it

Whom Shijie Wu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Alexis Conneau
3. Geoffrey E. Hinton
4. Jimmy Ba
5. Yoshua Bengio
6. Ilya Sutskever
7. Guillaume Lample
8. Jacob Devlin
9. Alec Radford
10. Tomas Mikolov

Who trusts Shijie Wu overall

1. Ivan Vulic
2. Jonas Pfeiffer
3. Kelechi Ogueji
4. Peng Shi
5. Alexis Conneau
6. Liunian Harold Li
7. Zewen Chi
8. Phillip Rust
9. Sebastian Ruder
10. Anna Rogers

Papers from Shijie Wu's perspective

Ordered by how strongly the researchers Shijie Wu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT (2019) — 753 citations
• Emerging Cross-lingual Structure in Pretrained Language Models (2019) — 329 citations
• Are All Languages Created Equal in Multilingual BERT? (2020) — 402 citations
• The SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion (2020) — 56 citations
• BloombergGPT: A Large Language Model for Finance (2023) — 1,395 citations
• SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection (2020) — 90 citations